There are many applications for Autonomous Seaborne Vessels (ASVs). The seaborne cargo shipping industry moves over 9 billion tons of cargo per year, is worth $375 billion, and is responsible for 90 percent of world trade. Autonomous cargo ships could reduce the operating expenses of cargo ships by 44%. ASVs can also be used by the military for surveillance, and for autopilot of pleasure ships.
One of the main obstacles to the development of ASVs is that they need to obey the International Regulations for Preventing Collisions at Sea 1972 (COLREGs). COLREGs governs when a vessel has the right of way over other vessels, and the rules depend on the kind of ship encountered. For example, a motorized vessel must give way to a sailing vessel and vessels engaged in fishing. To follow these rules, it is necessary for an ASV to categorize other vessels. An ASV may need to classify ships for other reasons as well. For example, a military ASV may need to categorize hostile military vessels to determine how to best escape conflict.
Gnosptic Fields for Maritime Imagery are capable of accurately classifying ships into 70 fine-grained categories and seven categories that are relevant to COLREGs. The software takes images of ships as input and classifies the ship at multiple levels, e.g., a large ship that is also a cargo vessel. The software accomplishes this using Gnostic Fields, a brain-inspired classification algorithm for images.
The software is implemented in MATLAB. The algorithm can operate with a limited amount of data. When trained, it can classify ships quickly on a modern system equipped with a GPU (i.e., about 14 images per second in a MATLAB implementation).
This work was done by Christopher Kanan of Caltech for NASA’s Jet Propulsion Laboratory.
The software used in this innovation is available for commercial licensing. Please contact Dan Broderick at
This Brief includes a Technical Support Package (TSP).
Ship Classification Using Gnostic Fields
(reference NPO49712) is currently available for download from the TSP library.
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Overview
The document titled "Technical Support Package for Ship Classification Using Gnostic Fields" presents research conducted by Christopher Kanan at the Jet Propulsion Laboratory (JPL), California Institute of Technology, under NASA's sponsorship. It focuses on the classification of ships and other objects in maritime environments, emphasizing the importance of accurate identification for safety and compliance with international maritime regulations, specifically the COLREGs (International Regulations for Preventing Collisions at Sea).
The document outlines the challenges associated with ship classification, which include significant variations in scale, rotation, and design among different ship types. These variations complicate the classification process, making it difficult to accurately identify vessels in diverse backgrounds. The research employs Gnostic Fields, a machine learning approach, which has demonstrated over 90% accuracy in classifying ships at a salient level. This method leverages color and other features to enhance classification performance, although the preliminary results indicate a need for more data, particularly for categories beyond cargo ships.
The document also highlights the necessity of classifying ships for two primary reasons: to adhere to COLREGs and to ensure the protection of vessels in busy maritime environments. By improving ship classification, the research aims to enhance the safety and efficiency of maritime navigation.
In summary, the document provides a comprehensive overview of the challenges and methodologies associated with ship classification using Gnostic Fields. It underscores the potential of this technology to improve maritime safety and compliance, while also acknowledging the need for further research and data collection to refine the classification system across various ship categories. The findings are positioned within the broader context of aerospace-related developments, suggesting that advancements in this area could have significant implications for both maritime and other technological applications.
